Perspective conversion for multi-dimensional data analysis

ABSTRACT

Multi-dimensional data can be mapped to a projection shape and converted for image analysis. In some examples, the multi-dimensional data may include data captured by a LIDAR system for use in conjunction with a perception system for an autonomous vehicle. Converting operations can include converting three-dimensional LIDAR data to multi-channel two-dimensional data. Data points of the multi-dimensional data can be mapped to a projection shape, such as a sphere. Characteristics of the projection shape may include a shape, a field of view, a resolution, and a projection type. After data is mapped to the projection shape, the projection shape can be converted to a multi-channel, two-dimensional image. Image segmentation and classification may be performed on the two-dimensional data. Further, segmentation information may be used to segment the three-dimensional LIDAR data, while a rendering plane may be positioned relative to the segmented data to perform classification on a per-object basis.

BACKGROUND

Image segmentation is one type of image analysis that is often used forpartitioning an image into different segments, or super-pixels, toprovide a more meaningful representation of the image. As one example,an image may be segmented so as to uniquely identify objects within theimage. Image segmentation on three-dimensional data may use regiongrowing techniques to examine neighboring pixels of initial seed pointsto determine whether the neighboring pixels should be added to theregion. However, in the past, these region growing techniques are oftenslow and may not provide accurate results.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is described with reference to the accompanyingfigures. In the figures, the left-most digit(s) of a reference numberidentifies the figure in which the reference number first appears. Theuse of the same reference numbers in different figures indicates similaror identical components or features.

FIG. 1 illustrates a pictorial flow diagram of an example process forconverting a three-dimensional LIDAR dataset to a projection shape,converting the projection shape to a multi-channel two-dimensionalimage, and performing segmentation and/or classification on theresulting multi-channel two-dimensional image.

FIG. 2 illustrates a pictorial flow diagram of an example process forcapturing three-dimensional data of an object, receiving segmentationinformation, adapting a rendering perspective for the object, convertingthe three-dimensional data of the object to two-dimensional data of theobject, and performing classification.

FIG. 3 illustrates an example architecture for implementing the dataconverting for image analysis, as described herein.

FIG. 4A depicts a side view of an example vehicle having multiple sensorassemblies mounted to the vehicle.

FIG. 4B depicts a top plane view of an example vehicle having multiplesensor assemblies mounted to the vehicle.

FIGS. 5A and 5B illustrate a pictorial flow diagram of an exampleprocess for converting multi-dimensional data for image analysis.

FIG. 6A illustrates various examples of projection shapes for use inconverting multi-dimensional data for image analysis.

FIG. 6B illustrates an example of a field of view associated with aspherical projection shape for use in converting multi-dimensional datafor image analysis.

FIG. 6C illustrates an example resolution associated with a sphericalprojection shape for use in converting multi-dimensional data for imageanalysis.

FIG. 6D illustrates an example of projection types for use in convertingmulti-dimensional data for image analysis.

FIGS. 7A and 7B illustrate an example of using a projection shape to mapdata associated with a vector to a projection area for use in convertingmulti-dimensional data for image analysis.

FIG. 8 is an example of unrolling a projection shape into amulti-channel image.

FIG. 9 is an example of combining data over time for incorporatingtemporal features into multi-channel data for image analysis.

FIG. 10A is a graphical representation of an example of altering aviewpoint for rendering three-dimensional segmented data for subsequentimage classification.

FIG. 10B is a plane view of a simplified representation of the graphicalrepresentation illustrated in FIG. 10A.

FIG. 10C is a plane view of an example of projecting three-dimensionalsegmented data onto a first plane.

FIG. 10D is a plane view of an example of projecting three-dimensionalsegmented data onto a rendering plane.

FIG. 11 depicts an example process for converting three-dimensionalenvironment data to a multi-channel image.

FIG. 12 depicts an example process for generating a trajectory for anautonomous vehicle based on image segmentation, as described herein.

FIG. 13 depicts an example process for receiving segmentationinformation, adapting a rendering plane for an object, convertingthree-dimensional data of the object to two-dimensional data of theobject, and performing classification.

FIG. 14 depicts a block diagram of an example computer system forimplementing the techniques described herein.

DETAILED DESCRIPTION

Three-dimensional data can be used in computer vision contexts to locateand interact with objects in the physical world. Often, an initial stepof such computer vision includes segmenting data representing theobjects to perform subsequent processing. Previous work on imagesegmentation has been directed to two-dimensional images, and oftenalgorithms developed for two-dimensional data cannot be adapted for usewith three-dimensional data. Thus, converting three-dimensional data totwo-dimensions may allow computer vision systems to leverage provenalgorithms that have been developed in the context of two-dimensionaldata.

This disclosure describes methods, apparatuses, and systems forconverting multi-dimensional data for image analysis. In some examples,the multi-dimensional data may include data captured by a LIDAR systemfor use in conjunction with a perception system for an autonomousvehicle. For example, a LIDAR system may have a light emitter and alight sensor, with the light emitter including one or more lasers thatdirect highly focused light toward an object or surface which reflectsthe light back to the light sensor. Measurements of the LIDAR system maybe represented as three-dimensional LIDAR data having coordinates (e.g.,Cartesian, polar, etc.) corresponding to positions or distances capturedby the LIDAR system. The converting operations described herein caninclude converting the three-dimensional LIDAR data to multi-channeltwo-dimensional data, and performing image segmentation andclassification on the two-dimensional data. Subsequently, thesegmentation and/or classification information may be used as input fordetermining a trajectory for the autonomous vehicle, for example.

As mentioned above, the three-dimensional LIDAR data can include a threedimensional map or point cloud which may be represented as a pluralityof vectors emanating from a light emitter and terminating at an objector surface. To convert the three-dimensional LIDAR data totwo-dimensional data, an example method can include mapping the LIDARdata to a three-dimensional projection shape and converting theprojection shape to a two-dimensional plane, while subsequentlyperforming segmentation and/or classification on the two-dimensionaldata. The projection shape can include, for example, a sphere, cube,cylinder, pyramid, etc., which can be placed (virtually) around theorigin of the three-dimensional data. Each vector or point of the pointcloud can be mapped to a cell or array of locations associated with theprojection shape. In some instances, each cell of the projection shapecan include coordinates for one or more vectors passing through thecell, and in some instances, each vector or point of the point cloud caninclude a range (e.g., distance between the light emitter and a point Pof the object or surface) and/or a surface normal vector associated witheach point P associated with each LIDAR vector.

The projection shape may include a number of characteristics, such asshape, field of view, resolution, and projection type. For example,shapes of the projection shape may include a sphere, cube, cylinder,pyramid, or any three-dimensional shape. The field of view may constrainan area above or below an origin of the LIDAR data, beyond which thedata may be omitted or ignored. For example, for a LIDAR sensor mountedon a roof of an autonomous vehicle, data above a field of view mayrepresent tops of buildings or the sky, and may be ignored forprocessing. Further, data below a field of view may represent a roof ofthe autonomous vehicle and may be ignored or used as a fixed referencepoint to calibrate the LIDAR sensor. Next, a horizontal resolution and avertical resolution may be selected for each projection shape. Forexample, a vertical resolution of the projection shape may vary based onan angle of elevation of the LIDAR system. That is, a resolution (e.g.,an amount of detail) may increase or decrease depending on a heightabove a horizon. By way of another example, a horizontal resolution mayvary based on a direction of the LIDAR data relative to a direction oftravel of the autonomous vehicle. For example, a resolution may behigher in a direction of travel (e.g., in front of a vehicle) and lowerin a direction away from the direction of travel (e.g., behind thevehicle). In some instances, a projection type may be associated withconverting or mapping the projection shape to a two-dimensional surface.For example, a first projection type may include perspective geometry,which may include projecting data from a cell of the projection shapealong a vector emanating from the LIDAR sensor origin to thetwo-dimensional surface. A second projection type may include orthogonalgeometry, which may include projecting data from a cell of theprojection shape along a vector orthogonal to the two-dimensionalsurface receiving the projection. Details of the projection shape aregiven with respect to FIGS. 6A, 6B, 6C, 6D, and other figures of thisdisclosure.

As mentioned above, after converting the three-dimensional LIDAR data totwo-dimensional data, the operations can include inputting thetwo-dimensional data into a convolutional neural network (CNN) forsegmentation and classification. The CNN may be included in a planningsystem for an autonomous vehicle to analyze the two-dimensional data todetermine free space for generating a trajectory for the autonomousvehicle. While examples herein describe using a CNN, in other examples,other machine learning techniques may additionally or alternatively beused for segmentation and/or classification.

In some instances, after performing segmentation on the two-dimensionaldata to generate segmentation information, the segmentation informationmay be applied to the three-dimensional LIDAR data for subsequentprocessing. For example, the segmentation information may include asegmentation identifier, identification (ID), or tag associated witheach point of the point cloud or pixel, and can be applied to thethree-dimensional information to identify three-dimensional dataassociated with an object. As a non-limiting example, all LIDAR pointsassociated with a single object may all have the same ID, whereas LIDARpoints associated with different objects may have different IDs. Afteridentifying the object in the three-dimensional data, thethree-dimensional data can be converted to two-dimensional data byprojecting the three-dimensional data onto a projection plane (alsoreferred to as a rendering plane), which may include adapting orpositioning a rendering perspective (e.g., the rendering plane) relativeto the object. Conceptually, adapting the rendering perspective to theobject can include viewing the three-dimensional data from a virtualperspective, such that the rendering perspective is not constrained bythe original location of the LIDAR sensor, for example. In practice, therendering perspective is selected to maximize a horizontal and/orvertical extent of the three-dimensional segmented data onto therendering plane. Upon determining the rendering perspective (and theassociated rendering plane), the operations can include projecting thethree-dimensional segmented data onto the two-dimensional renderingplane, and subsequently, the operations can include performingclassification on the flattened two-dimensional data to classify theobject represented by the data.

The converting operations and systems described herein can improve afunctioning of a computing device by converting data into one or moreformats that improves performance of segmentation and/or classificationof objects represented in the data. In some instances, the improvedconverting operations and systems may provide more accurate and/orfaster segmentation by converting three-dimensional data intotwo-dimensional data so that existing machine learning networks and/oralgorithms may be applied to the data for segmentation and/orclassification. Using the converting operations described herein todetermine segmentation information, the operations of applying thesegmentation information to three-dimensional data and applying arendering perspective on a per object basis leads to more accurateand/or faster classification of objects by exposing additional data to amachine learning algorithm. Further, the data conversion describedherein may allow for deep learning techniques, which provide improvedprocessing. In some instances, faster and/or more accurate segmentationand/or classification may be utilized in generating a trajectory of anautonomous vehicle, which may improve safety for occupants of anautonomous vehicle. In some instances, the converting operationsdescribed herein may reduce memory requirements or reduce an amount ofprocessing by applying machine learning operations (e.g., aconvolutional neural network) to simplified (e.g., segmented) data. Insome instances, stacking multiple channels of lower-dimensional images(e.g., over time) improves segmentation and/or classification byincorporating temporal information into the operations. These and otherimprovements to the functioning of the computer are discussed herein.

The methods, apparatuses, and systems described herein can beimplemented in a number of ways. Example implementations are providedbelow with reference to the following figures. Although discussed in thecontext LIDAR data and/or in the context of an autonomous vehicle, themethods, apparatuses, and systems described herein can be applied to avariety of systems utilizing machine vision. Further, although describedin connection with three-dimensional LIDAR data, the methods,apparatuses, and systems described herein are not limited tothree-dimensional data, and are not limited to LIDAR data. For example,the methods, apparatuses, and systems may be utilized in a manufacturingassembly line context, or in an aerial surveying context. Further, thedatasets may include data from stereoscopic cameras, depth cameras,Radar sensors, etc., and may include any number of layers or channels,which may correspond to any number of dimensions. Additionally, thetechniques described herein may be used with real data (e.g., capturedusing sensor(s)), simulated data (e.g., generated by a simulator), orany combination of the two.

FIG. 1 illustrates a pictorial flow diagram of an example process 100for mapping a three-dimensional LIDAR dataset to a projection shape,converting the projection shape to a multi-channel two-dimensionalimage, and performing segmentation and/or classification on theresulting multi-channel two-dimensional image.

At operation 102, the process can include receiving at least onethree-dimensional LIDAR dataset. In some instances, the operation 102may include receiving a plurality of LIDAR datasets from a plurality ofLIDAR sensors operated in connection with a perception system of anautonomous vehicle. In some instances, the operation 102 may includecombining or fusing data from two or more LIDAR sensors into a singleLIDAR dataset (also referred to as a “meta spin”). In some instances,the operation 102 may include extracting a portion of the LIDAR data forprocessing, such as over a period of time. In some instances, theoperation 102 may include receiving Radar data and associating the Radardata with the LIDAR data to generate a more detailed representation ofan environment. An example of a LIDAR dataset is illustrated in anexample 104, which may include LIDAR data (e.g., point clouds)associated with various objects in an urban environment, such as cars,trucks, roads, buildings, bikes, pedestrians, etc.

At operation 106, the process can include projecting thethree-dimensional LIDAR data onto a projection shape. As illustrated inexample 108, the operation 106 can include projecting thethree-dimensional LIDAR data (also referred to generally as “LIDARdata”) onto a spherical projection shape. In some instances, theoperation 106 can include selecting a particular shape as the projectionshape, and may include selecting a field of view, resolution, and/orprojection type. As illustrated in the example 108, the sphericalprojection shape may include any number of cells 110. In some instances,the operation 106 can include virtually locating the projection shapearound an origin of the three-dimensional LIDAR data, which in somecases, may correspond to a location of a LIDAR system, or may correspondto a virtual location of a LIDAR meta spin. That is, the operation 106may include defining the projection shape, algorithmically locating theprojection shape around the LIDAR data, and associating LIDAR pointswith a corresponding cell of the projection shape, such as the cell 110.In some instances, associating a LIDAR data point with a correspondingcell of the projection shape may include storing LIDAR data, such as (x,y, z) coordinates or polar coordinates of a point in a point cloud, inthe corresponding cell. In some instances, the operation 106 may includestoring range information (e.g., a distance from an origin of the vectorto a point P of an object or a surface) in addition to (x, y, z)coordinates, and in some instances, the operation 106 may includestoring a surface normal vector associated with each point P in thecell.

At operation 112, the process can include converting the projectionshape into a multi-channel two-dimensional image. In an example 114, theprojection shape is converted into a plurality of two-dimensional arrays116, 118, 120, and 122. In some instances, the two-dimensional arrays116, 118, 120, and 122 may be considered to be individual “images”, witheach image corresponding to an individual dimension of the LIDAR datastored in the cell 110 of the projection shape. For example, thetwo-dimensional array 116 may correspond to a range of each LIDAR datapoint associated with the projection shape, the two-dimensional array118 may correspond to the x-coordinate of each LIDAR data pointassociated with the projection shape, the two-dimensional array 120 maycorrespond to the y-coordinate of each LIDAR data point associated withthe projection shape, and the two-dimensional array 122 may correspondto the z-coordinate of each LIDAR data point associated with theprojection shape. In some instances, each of the two-dimensional arrays116, 118, 120, and 122 may be referred to as a channel, with thetwo-dimensional arrays 116, 118, 120, and 120 collectively referred toas a multi-channel two-dimensional image.

Of course, it may be understood that individual sets of multi-channeltwo-dimensional images may be generated at individual instants in time.That is, a first set of multi-channel two-dimensional images may begenerated at a first time, while a second set of multi-channeltwo-dimensional images may be generated at a second time. Further, theoperation 112 may include combining two or more sets of multi-channeltwo-dimensional images to create images with time as an additionalchannel.

At operation 124, the process may include performing segmentation and/orclassification on the multi-channel two-dimensional image. An example126 illustrates an output of one such segmentation operation, includingsegmentation information 128 associated with an object. In someinstances, the segmentation information 128 may include a segmentationidentification (ID) associated with each pixel or LIDAR data point, forexample, with a particular segmentation ID defining a particular object.In some instances, the multi-channel two-dimensional images may be inputto one or more machine learning networks, such as a convolutional neuralnetwork, to perform deep learning operations on the data to performtasks such as segmentation and/or classification. In some instances,after segmentation information has been generated, the segmentationinformation can be applied to three-dimensional data to isolate orsegment one or more objects for classification on a per object basis.Aspects of this additional object-based converting is described belowwith connection to FIG. 2, as well as other figures.

FIG. 2 illustrates a pictorial flow diagram of a process 200 forcapturing three-dimensional data of an object, receiving segmentationinformation, adapting a rendering perspective for the object, convertingthe three-dimensional data of the object to two-dimensional data of theobject, and performing classification. Aspects of the process 200 can beperformed in addition to or in conjunction with the converting andsegmenting operations described in connection with FIG. 1. In someinstances, the classification operations performed in FIG. 2 may beselectively applied when a confidence level of a classificationperformed as described in FIG. 1 is below a confidence threshold, or insome instances, the operations in FIG. 2 can be performed in parallel toverify or confirm other classifications.

At operation 202, the process can include capturing three-dimensionaldata of an object. In some instances, the operation 202 can includecapturing LIDAR data using one or more LIDAR sensor systems. In someinstances, the operation 202 can include capturing a meta spin of LIDARdata, which may include combining or synthesizing LIDAR data from aplurality of LIDAR sensors or other sensors. For example, all pointsreturned from multiple LIDAR systems may be transformed to a commoncoordinate system (e.g., a world coordinate system or a coordinatesystem relative to all LIDAR systems). As illustrated in example 204,the operation 202 can include capturing three-dimensional data of anobject 206 using a LIDAR sensor 208 associated with a perception systemof an autonomous vehicle 210. As illustrated, the capturing of LIDARdata is represented by the vectors 212 emanating from the LIDAR sensor208. Further, the vectors 212 may represent a field of view of the LIDARsensor 208, or a first perspective or direction of capturing data.Additionally, the operation 202 may include simulating at least aportion of the three-dimensional data, or receiving three-dimensionaldata from a simulator. An example of three-dimensional data of an objectis illustrated in FIG. 1 as the example 104.

At operation 214, the process can include receiving segmentationinformation associated with the three-dimensional data of the object. Insome instances, the segmentation information can be generated accordingto the process 100 illustrated in FIG. 1. In some instances, thesegmentation information generated in the process 100 may includesegmentation information associated with the two-dimensionalrepresentation of the three-dimensional data, in which case, thetwo-dimensional segmentation information may be converted tothree-dimensional segmentation information. In some instances, thesegmentation information may include a segmentation identification (ID)associated with each of the three-dimensional data points, which may beused in turn to determine which data points are to be associated with aparticular object for segmentation. In some instances, the operation 214may include receiving two-dimensional segmentation information andadding depth information to the segmentation information to capturedepth information of the three-dimensional data. An example 216illustrates segmentation information 218 applied to thethree-dimensional data 220 corresponding to the object 206. Asillustrated, the three-dimensional data 220 includes a point cloud,cluster, or blob of information associated with LIDAR data of the object206. In some instances, the operation 214 can include extracting orsegmenting the three-dimensional data 220 from a larger dataset toidentify or isolate the data associated with the object.

At operation 222, the process can include adapting a renderingperspective for the object. As illustrated in example 224, a renderingplane 226 is positioned with respect to the segmented data 220 tomaximize an extent of the segmented data 220 exposed to the renderingplane 226. As used herein, the rendering plane 226 may include a planeoriented relative to the segmented data 220 such that the segmented data220 may be projected onto the rendering plane 226 to convert thethree-dimensional data (e.g., the segmented data 220) into(multi-channel) two-dimensional data. For example, turning to theexample 204, the autonomous vehicle 210 is located relative to theobject 206 (illustrated as another car) such that the LIDAR sensor 208essentially captures a front view of the object 206. Because the LIDARsensor 208 may capture some depth aspects of the object 206 (e.g.,illustrated as data corresponding to the rear tire or the rear bumper inthe segmented data 220), the rendering plane 226 may represent adifferent perspective, as illustrated by the rendering perspective 228,such that when the segmented data 220 is projected onto the renderingplane 226, a horizontal extent and/or a vertical extent of projecteddata is maximized or optimized.

For example, the operation 222 may include determining a center of thesegmented data 220, which may include determining a “center of mass” ofthe data points of the segmented data 220. Next, the operation 222 mayinclude performing a principal component analysis of the segmented datato determine eigenvectors or principal components of the segmented data220. In some instances, a first principal component may correspond to anaxis of “maximum stretch” or variance of the segmented data 220. In someinstances, the first principal component can be selected or determinedas a principal component in the (x, y) plane (e.g., a horizontal plane).Next, a second principal axis can be selected as a principal componentorthogonal to the first principal component in a vertical direction(e.g., the z-direction). In some instances, an initial rendering planemay be defined by the first principal component and the second principalcomponent, and in some instances, the rendering plane (e.g., therendering plane 226) may be determined by rotating the initial renderingplane about the first principal component, which conceptually can beconsidered as increasing a height of the rendering perspective 228. Insome instances, the rendering plane 226 may be determined by rotatingthe initial rendering plane by a predetermined angle or number ofdegrees, or by selecting a predetermined height or a predeterminedchange in height for the rendering perspective 228, for example.Further, a center of the rendering plane 226 may be associated with acenter of mass of the segmented data 220, for example, following adetermination of the orientation of the rendering plane 226.

At operation 230, the process can include converting thethree-dimensional data of the object to two-dimensional data of theobject. As illustrated in example 232, the segmented data 220 may beconverted to two-dimensional segmented data 234 represented on arendering plane 236. As illustrated, the rendering plane 236 mayrepresent a plan view of the rendering plane 226, while the renderingplane 226 may represent a perspective view of the rendering plane 226.In some instances, the three-dimensional segmented data 220 may beprojected onto the rendering plane 236 to convert the three-dimensionalsegmented data 220 into the two-dimensional segmented data 234illustrated in the example 232. Similar to the process 100, thetwo-dimensional segmented data 234 may include multiple channels,including range (e.g., indicating a distance between a point P of thethree-dimensional segmented data 220 and the rendering perspective 228),(x, y, z) coordinates of the three-dimensional segmented data 220 thathas been re-parameterized with respect to the rendering perspective 228,a surface normal vector associated with each point of the segmented data220, time information, etc.

At operation 238, the process can include performing classification onthe-two dimensional data of the object. In some instances, the operation238 can include performing classification on the two-dimensionalsegmented data 234 that was projected onto the rendering plane 236 thatwas adapted or positioned according to the rendering perspective 228 tomaximize or optimize a horizontal and/or vertical extent of thesegmented data 220, as described herein.

FIG. 3 illustrates an example architecture for implementing the dataconversion for image analysis, as described herein. For example, thearchitecture 300 may include one or more computer system(s) 302including various hardware and/or software to implement aspects of thesystems, methods, and apparatuses described herein. For example, thecomputer system(s) 302 may include a LIDAR module 304, a camera module306, a Radar module 308, a projection shape module 310, a dimensionconversion module 312, a segmentation module 314, a classificationmodule 316, an object isolation module 318, a rendering perspectivemodule 320, and a trajectory module 322.

In some instances, the computer system(s) 302 may be embodied in anautonomous vehicle. In some instances, the computer system(s) 302 mayprovide perception and planning functionality for the autonomousvehicle. In general, the computer system(s) 302 may include LIDARperception, Radar perception, Vision (camera) perception, segmentationand classification, tracking and fusion, and prediction/planning.

Turning to the LIDAR module 304, the LIDAR module 304 may include one ormore LIDAR sensors to capture LIDAR data for image segmentation andclassification, as described herein. In some instances, the LIDAR module304 may include functionality to combine or synthesize LIDAR data from aplurality of LIDAR sensors to generate a meta spin of LIDAR data, whichmay refer to LIDAR data based on multiple LIDAR sensors. In the case ofa meta spin of LIDAR data, the LIDAR module 304 may includefunctionality to determine a virtual origin of the meta spin data. Insome instances, the LIDAR module 304 may include functionality todetermine a range between a LIDAR sensor and a point P of an object orsurface, and in some instances, the LIDAR module 304 may includefunctionality to determine a surface normal vector for each point Pcaptured and/or sensed by the LIDAR module 304. As a non-limitingexample, such a surface normal determination may be done by calculatingthe normal of the cross product of vectors indicating directions fromthe point, P, to two of P's nearest neighbors. As may understood in thecontext of this disclosure, the LIDAR module 304 may capture data andmay transmit datasets to the computer system(s) 302 for subsequentprocessing.

The camera module 306 may include one or more camera sensors to capturevision data for image segmentation and/or classification. The cameramodule 306 may include any number and type of camera sensors. Forexample, the camera module 306 may include any color cameras, monochromecameras, depth cameras, RGB-D cameras, stereo cameras, infrared (IR)cameras, ultraviolet (UV) camera, etc. As may understood in the contextof this disclosure, the camera module 306 may capture data and maytransmit datasets to the computer system(s) 302 for subsequentprocessing. For example, data from the camera module 306 may be includedas one or more channels of a multi-channel image.

The Radar module 308 may include one or more Radar sensors to capturerange, angle, and/or velocity of objects in an environment. As mayunderstood in the context of this disclosure, the Radar module 308 maycapture data and may transmit datasets to the computer system(s) 302 forsubsequent processing. For example, data from the Radar module 308 maybe included as one or more channels of a multi-channel image.

The computing system(s) 302 may include any number or type of othersensors suitable for use in an autonomous vehicle, for example. Varioussensors may include, but are not limited to, sonar sensors, ultrasonictransducers, wheel encoders, microphones, inertial measurement unit(s)(IMU), accelerometers, gyroscopes, magnetometers, temperature sensors,humidity sensors, light sensors, global positioning system (GPS)sensors, etc.

In some instances, the LIDAR module 304, the camera module 306, and theRadar module 308 may provide one or more datasets to the computersystem(s) 302 for combining and/or synthesizing the data for improvedimage segmentation and/or classification.

Further, the computer system(s) 302 may include functionality to receiveand store sensor datasets as described herein. In some instances, thecomputer system(s) 302 may include functionality to annotate the storeddata, which may include detecting, identifying, classifying, segmenting,labeling, etc. the data.

The computer system(s) 302 may further include simulated data that hasbeen generated by a computer simulation algorithm, for use in part intesting. In some instances, the simulated data may include any type ofsimulated data, such as camera data, LIDAR data, Radar data, GPS data,etc. In some instances, computer system(s) 302 can modify, transform,and/or perform the converting operations described herein on thesimulated data for verifying an operation and/or for training themachine learning algorithms, as described herein.

The projection shape module 310 may include functionality to determine aprojection shape for use in data converting, as described herein. Insome instances, the projection shape module 310 can determine aprojection shape, a field of view, a resolution, and/or a projectiontype in converting three-dimensional data to multi-channeltwo-dimensional data. In some instances, the projection shape module 310can select one or more characteristics of the projection shape tooptimize performance of image segmentation and/or classification, tooptimize a processing time, memory requirements, etc. Additional detailsof the projection shape that can be selected or modified by theprojection shape module 310 are discussed below in connection with FIGS.6A, 6B, 6C, and 6D.

The dimensionality conversion module 312 may include functionality toconvert, transform, or map data having a first dimensionality to datahaving a second dimensionality. In some instances, the dimensionalityconversion module 312 may convert one or more three-dimensional LIDARdatasets to one or more multi-channel two-dimensional images. Forexample, the dimensionality conversion module 312 may include virtuallylocating a projection shape around a portion of three-dimensional data,associating one or more data points with various cells of the projectionshape, and unrolling, converting, or converting data from the projectionshape to the one or more multi-channel two-dimensional images. Thedimensionality conversion module 312 may perform any conversionoperations to convert the three-dimensional data to two-dimensionaldata, including but not limited to spherical projections (e.g.,stereographic and cylindrical), Mercator projection, direct polarconversion (e.g., spherical or equirectangular projection), etc.Additional aspects of the dimensionality conversion module 312 areprovided throughout this disclosure.

The segmentation module 314 may include functionality to performsegmentation on one or more multi-channel two-dimensional images. Forexample, the segmentation module 314 may input the one or moremulti-channel two-dimensional images to or more machine learningalgorithms. For example, the segmentation module 314 (also referred toas a “segmenter”) may perform image segmentation to segment objectsrepresented in the data for subsequent image classification. In someinstances, any hardware and/or software configured to performsegmentation operations on data may be considered to be a segmenter. Insome instances, the segmentation module 314 may operate on any number ofchannels associated with the two-dimensional images. For example, thesegmentation module 314 may receive one or more channels as inputsincluding, but not limited to, range channels, x-axis channels, y-axischannels, z-axis channels, surface normal vector channels, reflectivitychannels, time channels, etc. In some instances, the segmentation module314 may use any machine learning algorithm. In particular, thesegmentation module 314 may utilize a convolutional neural networktrained to segment multi-channel two-dimensional data representing LIDARdata, for example.

The classification module 316 may include functionality to receivesegmented data and to identify a type of object represented by the data.For example, the classification module 316 may classify one or moreobjects, including but not limited to cars, buildings, pedestrians,bicycles, trees, free space, occupied space, street signs, lanemarkings, etc. In some instances, the classification module 316 mayperform classification on data segmented using the operations describedin the process 100, and/or may perform classification on data segmentedusing the operations described in the process 200. The classificationmodule 316 and/or the segmentation module 314 may comprise any machinelearning algorithms such as neural networks to perform operations ofsegmentation and classification.

As described herein, an exemplary neural network is a biologicallyinspired algorithm which passes input data through a series of connectedlayers to produce an output. One example of a neural network may includea convolutional neural network, or CNN. Each layer in a CNN may alsocomprise another CNN, or may comprise any number of layers. Details areprovided below in connection with FIG. 9. As may be understood in thecontext of this disclosure, a neural network may utilize machinelearning, which may refer to a broad class of such algorithms in whichan output is generated based on learned parameters.

Although discussed in the context of neural networks, any type ofmachine learning may be used consistent with this disclosure. Forexample, machine learning algorithms may include, but are not limitedto, regression algorithms (e.g., ordinary least squares regression(OLSR), linear regression, logistic regression, stepwise regression,multivariate adaptive regression splines (MARS), locally estimatedscatterplot smoothing (LOESS)), instance-based algorithms (e.g., ridgeregression, least absolute shrinkage and selection operator (LASSO),elastic net, least-angle regression (LARS)), decisions tree algorithms(e.g., classification and regression tree (CART), iterative dichotomiser3 (ID3), Chi-squared automatic interaction detection (CHAID), decisionstump, conditional decision trees), Bayesian algorithms (e.g., naïveBayes, Gaussian naïve Bayes, multinomial naïve Bayes, averageone-dependence estimators (AODE), Bayesian belief network (BNN),Bayesian networks), clustering algorithms (e.g., k-means, k-medians,expectation maximization (EM), hierarchical clustering), associationrule learning algorithms (e.g., perceptron, back-propagation, hopfieldnetwork, Radial Basis Function Network (RBFN)), deep learning algorithms(e.g., Deep Boltzmann Machine (DBM), Deep Belief Networks (DBN),Convolutional Neural Network (CNN), Stacked Auto-Encoders),Dimensionality Reduction Algorithms (e.g., Principal Component Analysis(PCA), Principal Component Regression (PCR), Partial Least SquaresRegression (PLSR), Sammon Mapping, Multidimensional Scaling (MDS),Projection Pursuit, Linear Discriminant Analysis (LDA), MixtureDiscriminant Analysis (MDA), Quadratic Discriminant Analysis (QDA),Flexible Discriminant Analysis (FDA)), Ensemble Algorithms (e.g.,Boosting, Bootstrapped Aggregation (Bagging), AdaBoost, StackedGeneralization (blending), Gradient Boosting Machines (GBM), GradientBoosted Regression Trees (GBRT), Random Forest), SVM (support vectormachine), supervised learning, unsupervised learning, semi-supervisedlearning, etc.

The object isolation module 318 may include functionality to receivesegmentation data and apply the segmentation data to three-dimensionalLIDAR data, for example, to isolate or extract LIDAR data for subsequentprocessing. For instance, the object isolation module 318 can isolate asegmented object for subsequent classification. In some instances, theobject isolation module 318 can receive two-dimensional segmentationdata and convert the two-dimensional segmentation data tothree-dimensional segmentation data to isolate an object represented bythree-dimensional data. In some instances, the object isolation module318 may operate to isolate an object for classification on a per objectbasis (e.g., based on a segmentation identification (ID) associated witheach data point).

The rendering perspective module 320 may include functionality to altera perspective of a rendering plane to convert three-dimensional data ofan object to two-dimensional data for subsequent classification. Asillustrated in FIG. 2, for example, the rendering perspective module 320may determine a virtual LIDAR perspective such that, when projectingthree-dimensional data onto a rendering plane, a horizontal extent ofthe data and/or a vertical extent of the data is maximized or optimized.In some instances, the rendering perspective module can determine aheight and range of the virtual LIDAR sensor from the rendering plane.In some instances, the size and/or location of the rendering perspectiveand/or the rendering plane may depend on a size of image to be input toa classifier. For example, if a classifier receives as input images witha size of 32 pixels by 32 pixels, the rendering perspective module 320may alter a perspective of the virtual LIDAR sensor to capture a widthof a segmented object with the pixel constraint defined by theclassification input image size. Additional details of the renderingperspective module 320 are provided in connection with FIGS. 2 and 10,as well as throughout this disclosure.

The trajectory module 322 may include functionality to receive segmentedand/or classified data to determine a trajectory of an autonomousvehicle to operate is a safe manner. For example, the trajectory module322 may receive image segmentation and/or classification informationidentifying free space on a road for an autonomous vehicle to travel,and generate a trajectory for the autonomous vehicle to follow. In someinstances, the trajectory module 322 may receive as inputs the segmentedand/or classified objects as discussed herein and may track objects togenerate a trajectory based at least in part on such objects.

Additional details of the computer system(s) 302 are provided below inconnection with FIG. 14.

FIG. 4A depicts a side view 400 of an example vehicle 402 havingmultiple sensor assemblies mounted to the vehicle 402. In someinstances, datasets from the multiple sensor assemblies can be combinedor synthesized to form a meta spin (e.g., LIDAR data representing aplurality of LIDAR sensors) or can be combined or fused using sensorfusion techniques to improve an accuracy or processing for segmentation,classification, prediction, planning, trajectory generation, etc.

As shown in the side view 400, the vehicle 402 may include any number ofsensors in any combination or configuration. For example, the vehicle402 includes at least sensors 404, 406, and 408. In some instances, thesensor 404 may comprise a Radar sensor having a vertical field of viewillustrated as Θ₁. The sensor 406 may include a LIDAR sensor mounted ona roof of the vehicle 402, the sensor 406 having a vertical field ofview illustrated as Θ₂. In some instances, the sensor 408 may include acamera sensor having a vertical field of view Θ₃. Of course, the vehicle402 may include any number and type of sensors, and is not limited tothe examples provided in connection with FIG. 4A.

FIG. 4B depicts a top plane view 410 of the example vehicle 402 havingmultiple sensor assemblies mounted to the vehicle. For example, thesensors 404, 406, and 408 can be seen in FIG. 4B, as well as additionalsensors 412, 414, 416, and 418. For example, the sensors 408 and 418 maybe co-located or located proximate to one another, but may includedifferent sensor types or modalities, having various fields of view. Insome instances, the sensors 412, 414, 416, and 418 may includeadditional LIDAR, Radar, and/or camera sensors. As may be understood inthe context of this disclosure, the vehicle 402 may include any numberand any type of sensors. As illustrated in FIG. 4B, the sensor 404 mayinclude a horizontal field of view Θ₄, the sensor 406 may include ahorizontal field of view Θ₅, the sensor 408 may include a horizontalfield of view Θ₆, the sensor 412 may include a horizontal field of viewΘ₇, the sensor 414 may include a horizontal field of view Θ₈, the sensor416 may include a horizontal field of view Θ₉, and the sensor 418 mayinclude a horizontal field of view Θ₁₀. As may be understood in thecontext of this disclosure, the mounting locations and fields of viewmay include any number of configurations.

FIGS. 1, 2, 5A, 5B, 8, and 11-13 illustrate example processes inaccordance with embodiments of the disclosure. These processes areillustrated as logical flow graphs, each operation of which represents asequence of operations that can be implemented in hardware, software, ora combination thereof. In the context of software, the operationsrepresent computer-executable instructions stored on one or morecomputer-readable storage media that, when executed by one or moreprocessors, perform the recited operations. Generally,computer-executable instructions include routines, programs, objects,components, data structures, and the like that perform particularfunctions or implement particular abstract data types. The order inwhich the operations are described is not intended to be construed as alimitation, and any number of the described operations can be combinedin any order and/or in parallel to implement the processes.

FIGS. 5A and 5B illustrate a pictorial flow diagram of a process 500 forconverting multi-dimensional data for image analysis. For example, someor all of the process 500 can be performed by one or more components inthe architecture 300, or in the environment 1400, as described below.

At operation 502, the process may include receiving three-dimensionaldata. In some instances, the three-dimensional data may include LIDARdata from one or more sensors. In some instances, the three-dimensionaldata may include fused sensor data including LIDAR data and Radar data.In some instances, the data received in the operation 502 is not limitedto three dimensions, and may include any number of dimensions. Arepresentation of three-dimensional data is illustrated as an example504, which represents a point cloud of LIDAR data in an urbanenvironment, including one or more cars 506 and 508, buildings 510, etc.

At operation 512, the process may include extracting at least a portionof the three-dimensional data. For example, as illustrated in theexample 504, a segment 514 may represent a path or trajectory of anautonomous vehicle taken over a period of time, the period of timecorresponding to a time associated with the portion of thethree-dimensional data extracted in the operation 512. That is, thesegment 514 represents a period of time, and an example 516 maycorrespond to the three-dimensional data over that period of time. Asillustrated, the example 516 may include three-dimensional data 518 and520 (e.g., point clouds), which may correspond to the cars 506 and 508illustrated in the example 504. In some instances, an origin of thethree-dimensional data (e.g., the one or more LIDAR sensors) may berepresented as an origin 522. For example, the origin 522 may correspondto an origin of the vehicle, may correspond to a location of a singlesensor, or may correspond to a “center of mass” of all the contributingsensors (e.g., in the context of a meta spin, as discussed above).

Turning to FIG. 5B, the process 500 continues with the flow diagram 524.

At operation 526, the process may include determining a projectionshape. As illustrated, a projection shape 528 may include a sphericalprojection shape having a field of view, a resolution, and an associatedprojection type. Additional details of the projection shape 528 anddetermining the projection shape (e.g., in the operation 526) areprovided with respect to FIGS. 6A, 6B, 6C, and 6D.

At operation 530, the process may include projecting data associatedwith the three-dimensional data onto the projection shape. In someinstances, the operation 530 may include projecting a portion of thethree-dimensional data (e.g., extracted in the operation 512) onto theprojection shape. As illustrated in the example 516, the projectionshape 528 is virtually located around the origin 522 of thethree-dimensional data. Further, the operation 530 may include mappingdata associated with the three-dimensional data to individual cells ofthe projection shape. Additional details of mapping data onto theprojection shape are provided with respect to FIGS. 7A and 7B.

At operation 532, the process may include converting the projectionshape into a two-dimensional image. For example, the operation 532 mayuse any conversion techniques, including but not limited to sphericalprojections (e.g., stereographic and cylindrical), Mercator projection,direct polar conversion (e.g., spherical or equirectangular projection),etc. As illustrated in example 534, the projection shape 528 has beenconverted to the two-dimensional image, with various point clusters 536and 538 representing three-dimensional data converted to two-dimensionaldata. In some instances, the point clusters 536 and 538 may correspondto the three-dimensional data 518 and 520, respectively.

At operation 540, the process may include inputting the two-dimensional(multi-channel) image into a convolutional neural network for imagesegmentation. An example 542 illustrates an output of the operation 540,illustrating segmentation information 544 and 546. In some cases, thesegmentation information 544 and 546 may correspond to the point clouds536 and 538 of the example 534, respectively. In some instances, anysuitable machine learning algorithm may be used for segmentation.Further, the operation 540 may include inputting the segmentationinformation and/or the two-dimensional (multi-channel) image into aconvolutional neural network (or any machine learning algorithm) forclassification. In some instances, the operation 540 may includeproviding the segmentation information 544 and 546 to anotherclassification operation that may adapt a rendering perspective on a perobject basis, as discussed herein. In some instances, the operation 540may selectively invoke the segmentation and classification on aper-object basis, or may perform classification on the two-dimensionalimage, as described herein.

FIG. 6A illustrates various examples 600 of projection shapes for use inconverting multi-dimensional data for image analysis. Exemplaryprojection shapes include a spherical projection shape 602, acylindrical projection shape 604, a cubic or rectangular projectionshape 606, a pyramidal projection shape 608, etc. It may be understoodthat any shape may be used as a projection shape, and that theprojection shapes are not limited to the examples described above. Forexample, a projection shape may include a shape that such as a sphere ortriangle with a top and/or bottom cut off. Further associated with thespherical projection shape 602, the spherical projection shape 602 mayinclude an associated origin point 610 and a radius 612 which define, inpart, a location of the spherical projection shape 602 and a size of thespherical projection shape 602 when using the converting operationsdescribed herein. For example, the origin point 610 may be locatedproximate to the origin 522 of FIG. 5B when associating the sphericalprojection shape 602 with the three-dimensional data. In some instances,the origin point 610 may be collocated with the center of the sphererepresenting the spherical projection shape 602, although in othercases, the origin point 610 may be at any location within the interiorof the spherical projection shape 602. As may be understood, thedimensions of a particular projection shape may depend on a variety offactors, and may be based in part on a type of projection shape. Forexample, an ellipsoid projection shape may have a major axis and a minoraxis; the cylindrical projection shape 604 may have a height of thecylinder, a radius of the cylinder, and a height of the origin point,for example.

FIG. 6B illustrates an example of a field of view associated with aspherical projection shape 602 for use in converting multi-dimensionaldata for image analysis. In some instances, a LIDAR sensor may beinstalled on a roof of a vehicle, such that when aligning ortransforming the origin point 610 of the spherical projection shape 602with the LIDAR sensor (e.g., the LIDAR sensor 406 of FIG. 4A), LIDARsensor data may be available for spaces below a horizon point 614 andabove the horizon point 614. Thus, the spherical projection shape 602(and any projection shape) may have an associated upper field of view616 above the horizon and a lower field of view 618 below the horizon.For example, the upper field of view 616 may be defined, in part, by anangle Θ₁, while the lower field of view 618 may be defined, in part, byan angle Θ₂. In some instances, data outside the field of view may beignored, discarded, omitted, etc., for the purpose of mapping and/orconverting data for image analysis. In some instances, a field of viewmay differ depending on a portion of the projection shape. For example,for a portion of the projection shape towards a front of a vehicle(e.g., in a direction of travel), the field of view may be larger than afield of view for a portion of the projection shape towards a back of avehicle (e.g., a direction opposite the direction of travel). In someinstances, restricting a field of view improves a functioning of acomputer by reducing an amount of data for processing.

FIG. 6C illustrates an example of a resolution associated with aspherical projection shape 602 for use in converting multi-dimensionaldata for image analysis. For example, the spherical projection shape 602has an associated resolution 620 defining a size of cells of theprojection shape (e.g., as illustrated by the example 108 and the cell110). In some instances, an azimuthal (e.g., horizontal) resolution maybe highest nearest a horizon, which in the context of an autonomousvehicle, may represent data closest to a street level. In someinstances, an azimuthal resolution and an elevation (e.g., vertical)resolution may vary independently. In some instances, a resolution maybe highest in a direction of travel of an autonomous vehicle. Althoughthe resolution 620 is illustrated above the horizon point 614, it may beunderstood in the context of this disclosure that a resolution may varyfor any direction, aspect, or dimension of the projection shape, forexample. In some instances, a resolution may be highest near the horizonpoint 614, and may gradually decrease moving away from the horizon point614, as illustrated. In some instances, a resolution may be fixed orconstant for some or all portions of the projection shape. Additionalaspects of the resolution may be understood in connection with thevarious figures of this disclosure.

FIG. 6D illustrates an example of projection types for use in convertingmulti-dimensional data for image analysis. In some instances, a cell 622of the spherical projection shape may be associated with threedimensional information, as described herein. In converting theprojection shape to a two-dimensional surface, a number of projectiontechniques may be used. For example, using a cylindrical projectiontechnique, a cylinder 624 can be envisioned as surrounding the sphericalprojection shape 602. In projecting the data associated with the cell622 to the cylinder 624, perspective geometry or orthogonal geometry maybe used. For example, in the case of perspective geometry, dataassociated with the cell 622 can be projected onto the cylinder 624 byextending a vector 626 from the origin point to the cell 622 as aprojection vector 628. In a case of orthogonal geometry, an orthogonalvector 630 can extend from the cell 622 to the cylinder 624 to map thedata in the cell 622 to the cylinder 624. As may be understood, anynumber of projection techniques may be used to convert data associatedwith a particular projection shape to a two-dimensional image.

FIGS. 7A and 7B illustrate an example of using a projection shape to mapdata associated with a vector to a projection area for use in convertingmulti-dimensional data for image analysis.

FIG. 7A illustrates an example 700 of an autonomous vehicle 702 using aLIDAR sensor 704 to capture LIDAR associated with one or moremeasurements of a building 706. For example, a vector 708 from the LIDARsensor 704 to a point P 710 is captured as a measurement associated withthe building 706. In some instances, the vector 708 may be associatedwith (x, y, z) coordinate information, a time of the measurement, alocation of the autonomous vehicle 702, a distance of the vector 708(e.g., a range), a surface normal vector 712 associated with point P710, etc. As may be understood, the LIDAR sensor 704 may be capturingthousands or millions of points per second, at varying resolutions,frame rates, etc. In some instances, the operations illustrated in theexample 700 may correspond to an operation of capturingthree-dimensional LIDAR data.

FIG. 7B illustrates an example 714 of determining a projection shape andprojecting the three-dimensional data onto the projection shape. Theexample 714 illustrates a partial spherical projection shape 716 havingat least one cell 718 through which the vector 708 passes. As describedabove, the vector 708 may be associated with captured data such as (x,y, z) coordinates of the point P 710 on the building 706. Further, thevector 708 maybe associated with the surface normal vector 712associated with the point P 710. The information associated with thevector 708 may be stored in the cell 718 and further processed forconverting, as described herein.

FIG. 8 is an example 800 of a process for unrolling a projection shapeinto a multi-channel image. The example 800 illustrates a sphericalprojection shape 802 including a cell 804 with three-dimensional dataincluding range and (x, y, z) coordinates. As may be understood, thespherical projection shape 802 may include any number of cellsrepresenting LIDAR data projected onto the projection shape, asdescribed herein.

At operation 806, the process includes unrolling a projection shape intoa multi-channel image. For example, as discussed with respect to thespherical projection shape 802, each cell of the spherical projectionshape 802 may be associated with LIDAR data. In some instances, variouselements of data may be associated with an individual channel, such thateach channel of data may be unrolled into a separate channel. Forexample, range data of the spherical projection shape may be stored in arange channel 808. Similarly, the (x, y, z) coordinate data may bestored in a respective one of an x-channel 810, a y-channel 812, and az-channel 814. As may be understood, a multi-channel image may includeany number of channels and is not limited to the examples given in FIG.8. For example, a channel may include information associated withsurface normal vectors of captured points. By way of another example, achannel may include information associated with a camera sensor, Radardata, GPS data, time of day, segmentation ID, etc.

FIG. 9 is an example 900 of combining data over time for incorporatingtemporal features into multi-channel data for image analysis. Forexample, as described above, three-dimensional data can be projectedonto a projection shape and unrolled into a multi-channel image,comprised of individual channels 902, 904, 906, and 908. In someinstances, the channels 902, 904, 906, and 908 may correspond to thechannels 808, 810, 812, and 814, respectively, at a time T₀. That is,the channels 902, 904, 906, and 908 may represent two-dimensional dataof an instant of time represented as T₀. Similarly, at a time T₁, whichis a time different than T₀, three-dimensional LIDAR data may be mappedto individual channels 910, 912, 914, and 916. As may be understood inthe context of this disclosure, the channels associated with time T₀ maybe input to a convolutional neural network for segmentation and/orclassification, while the channels associated with time T₁ may beseparately input to the convolutional neural network for subsequentsegmentation and/or classification. However, in some instances, asillustrated in example 918, data over various time periods may bestacked or combined as multi-channel data and input to a convolutionalneural network for image segmentation and/or classification. In thismanner, temporal information may be incorporated into segmentationand/or classification.

As may be understood, data reflecting any amount of time may be utilizedin segmentation and/or classification. In some instances, data may begrouped according to a sliding time window for input to a convolutionalneural network. For example, at a first time, multi-channel datarepresenting times T₀, T₁, and T₂ may be input to a convolutional neuralnetwork. Subsequently, at a second time, multi-channel data representingtimes T₂, and T₃ may be input. At a third time, data representing T₂,T₃, and T₄ may be input, and so on. In some instances, a window may hop(e.g., include non-overlapping periods of time) instead of slide (e.g.,include overlapping periods of time).

FIG. 10A is a graphical representation 1000 of altering a viewpoint forrendering three dimensional segmented data for subsequent imageclassification. As may be understood, aspects of FIG. 10A may beexplained with reference to FIG. 2 and other figures. In some instances,FIG. 10A is a detail of the examples 216 and 224.

For example, FIG. 10A illustrates an autonomous vehicle 1002 including aLIDAR sensor 1004 for capturing three-dimensional LIDAR data 1006. Asdiscussed in this disclosure, the three-dimensional LIDAR data 1006 maybe captured by a perception system of the autonomous vehicle 1002 forgenerating a trajectory of the autonomous vehicle 1002. FIG. 10Aillustrates a perspective view in an (x, y, z) coordinate system,although any coordinate system may be used.

As discussed above with respect to FIG. 1, the converting operationsdescribed herein may perform segmentation on three-dimensional LIDARdata to determine locations of objects. In some instances, segmentationdata 1008 include two-dimensional segmentation data, three-dimensionalsegmentation data, segmentation identification (ID) that discretelyassociate data points with particular objects, etc. The segmentationdata 1008 can be applied to the three-dimensional LIDAR data 1006 toidentify and isolate data on a per object basis. For example, the LIDARdata 1006 may only include LIDAR data associated with a particularsegmentation ID, or may include only the LIDAR data inside a threedimensional bounding box output from the segmentation algorithm. Havingisolated (e.g., segmented) the three-dimensional LIDAR data 1006 usingthe segmentation data 1008, a rendering plane 1010 can be adapted tomaximize or optimize an extent of a projection of the three-dimensionalLIDAR data 1006 projected onto the rendering plane 1010.

In some instances, adapting the rendering plane 1010 may includepositioning a rendering plane 1010 relative to the LIDAR data 1006 basedat least in part on a principal component of the LIDAR data 1006associated with the segmentation data 1008. As discussed above, aninitial rendering plane can be define by a first principal component anda second principal component of the LIDAR data 1006. Further, theinitial rendering plane can be “rotated” upwards (relative to theground) by a predetermined angle or number of degrees. Conceptually,adapting the rendering plane 110 may include locating a virtual LIDARsensor 1012 at a point 1014 that is a distance D 1016 from the renderingplane 1010 and a height H 1018 above a ground plane representing a loweredge of the rendering plane 1010. Further, the location of the point1014 can be defined by the principal component analysis, as discussedherein.

In some instances, a size of the rendering plane 1010 may be based on anexpected input size of a two-dimensional image to be input to aconvolutional neural network for subsequent processing (e.g.,classification). Thus, the distance D 1016 can be selected for eachobject such that the horizontal expanse of the three-dimensional LIDARdata 1006 is maximized or optimized when projected onto the renderingplane.

Further, the rendering plane 1010 may be characterized based on one ormore angles of a surface normal vector of the rendering plane 1010relative to the LIDAR sensor 1004. For example, for a particular vectoremanating from the LIDAR sensor 1004 towards the three-dimensional LIDARdata 1006, the rendering plane 1010 may be oriented such that thesurface normal vector of the rendering plane 1010 is offset in anx-direction and/or a y-direction. A direction of the surface normalvector of the rendering plane 1010 may be based at least in part on aprincipal component analysis of the LIDAR data 1006 to maximize oroptimize on a per object basis a horizontal or vertical extent of thethree-dimensional LIDAR data 1006 when projected onto the renderingplane 1010.

FIG. 10B is a plane view 1020 of a simplified representation of thegraphical representation illustrated in FIG. 10A. As illustrated, FIG.10B represents a two-dimensional view (e.g., viewing the (x, y) axes) ofFIG. 10A. The simplified data 1006′ represents simplified data comparedto the LIDAR data 1006 illustrated in FIG. 10A.

FIG. 10C is a plane view 1022 of an example of projectingthree-dimensional segmented data onto a first plane. In one example, afirst plane 1024 represents a plane having a normal vector oriented in adirection of the LIDAR sensor 1004, which is to say a normal vector 1040of the first plane 1024 points towards the LIDAR sensor 1004. The LIDARdata 1006′ may be orthographically or geometrically projected onto thefirst plane 1024. For example, in an example 1026, the LIDAR data 1006′is projected onto the first plane 1024 such that the projected data 1028has a horizontal extent 1030.

FIG. 10D is a plane view 1032 of an example of projectingthree-dimensional segmented data onto a rendering plane. For example,the rendering plane 1010 is illustrated as plane with a normal vector1042 in a direction of the point 1014. In 1034, the LIDAR data 1006′ canbe orthographically or geometrically projected onto the rendering plane1010 such that the LIDAR data 1006′ projected as projected data 1036 hasa horizontal extent 1038. In some instances, the orientation of therendering plane (e.g., determined by the point 1014) is based at leastin part on a principal component analysis of the LIDAR data 1006′ tomaximize an extent of the horizontal extent 1038 (further subject to oneor more rotations, as discussed herein). In some instances, theorientation can be selected to maximize a variance of the projected data1036 in any direction.

In some instances, the LIDAR sensor 1004 represented in FIG. 10C may beconsidered to have or be oriented with respect to first perspective, andthe point 1014 may be considered to have or be oriented with respect toa second perspective. Further, it may be understood in the context ofthis disclosure that a vertical extent may be maximized or optimized(e.g., with respect to an (x, z) or (y, z) representation of the data).In some instances, a first variance (e.g., associated with horizontaland/or vertical spread) of data can be determined with respect to dataprojected onto a plane associated with the first perspective and asecond variance can be determined with respect to data projected ontothe rendering plane associated with the second perspective. In someinstances, the second perspective can be selected to substantiallymaximize the second variance. In some instances, a principal componentanalysis is performed on the segmented data to determine eigenvectorsassociated with the segmented data. In some instances, the principalcomponent analysis can determine eigenvectors representing variance ofthe segmented data in each dimension.

FIG. 11 depicts an example process 1100 for converting three-dimensionalenvironment data to a multi-channel image. For example, some or all ofthe process 1100 can be performed by one or more components in thearchitecture 300, or in the environment 1400, as described below.

At operation 1102, the process can include receiving at least onemulti-dimensional dataset. In some instances, the three-dimensionaldataset may include data from one LIDAR sensor or from many LIDARsensors. In some instances, the at least one multi-dimensional datasetmay represent real world data (e.g., captured by a sensor) or mayrepresent data generated in a simulator. In some instances, theoperation 1102 may include isolating or identifying an instant of timeor a range of time of the data for processing.

At operation 1104, the process can include determining a projectionshape. As discussed herein, determining a projection shape may includedetermining a particular shape, a field of view, a resolution, and aprojection type. Aspects of determining a projection shape are describedherein with respect to FIGS. 3, 6A, 6B, 6C, and 6D, for example.

At operation 1106, the process can include converting points of the atleast one multi-dimensional dataset to the projection shape. Thisoperation 1106 may include determining vectors associated with themulti-dimensional data and determining a cell of the projection shapeintersected by the data vectors. In some instances, for example, when adata point of the three-dimensional dataset is located on the interiorof the projection shape, the operation may include extending a vectorassociated with the data point to determine an intersection with a cellof the projection shape. In an instance when a data point of thethree-dimensional dataset is located external to the projection shape,the operation may include determining which cell of the projection shapeis intersected by the vector associated with the data point. Further,the operation 1106 may include storing measurements associated with thethree-dimensional dataset within each cell of the projection shape. Insome instances, where multiple vectors intersect with a cell, data maybe averaged, discarded, a resolution may be increased, etc. In someinstances, data corresponding to multiple vectors (and multiple datapoints, accordingly) may be stored in a single cell.

At operation 1108, the process can include mapping points of theprojection shape to a map plane (e.g., a cylinder). For example, theoperation 1108 may include determining a projection type or applying aprojection type, such as perspective geometry or orthogonal geometry, tomap the data in the projection shape to a cylinder.

At operation 1110, the process can include unrolling the map plane(e.g., the cylinder) into a multi-channel image. Examples of thisoperation 1110 are provided with respect to FIG. 8.

FIG. 12 depicts an example process 1200 for generating a trajectory foran autonomous vehicle based on image segmentation, as discussed herein.For example, some or all of the process 1200 can be performed by one ormore components in the architecture 300, or in the environment 1400, asdescribed below.

At operation 1202, the process may include performing segmentation onconverted data. For example, the converted data may refer tothree-dimensional data that has been converted to a multi-channeltwo-dimensional image, as described herein. In some instances, theconverted data is input into a convolutional neural network that istrained to segment images based on free space (e.g., drivable ornavigable space) in the input image. In some instances, theconvolutional neural network may identify objects to be tracked byvarious systems of the autonomous vehicle. As an example, the converteddata may be generated from an image capture system (e.g., a perceptionsystem) onboard an autonomous vehicle. In some instances, the imagecapture system may include any number of sensors, including but notlimited to image sensors, LIDAR, radar, etc.

At operation 1204, the process may include receiving one or more imagesthat have been segmented to create a set of images segmented for freespace, while in some instances, the operation 1004 may include receivingindications of one or more objects identified in the segmented images toperform object tracking and/or object motion prediction. At operation1206, the process can include inputting the images segmented for freespace or inputting the identified and/or tracked objects into a plannersystem, to generate a trajectory for the autonomous vehicle. In someinstances, the planner system may be incorporated into a computingsystem to receive free space segmented images or to receive objects tobe tracked and to generate a trajectory based at least in part on thesegmented images or tracked objects. At operation 1208, the process mayinclude generating a sequence of commands to command the autonomousvehicle to drive along the trajectory generated in operation 1206. Insome instances, the trajectory generated in the operation 1206 mayconstrain the operation of the autonomous vehicle to operate within thefree space segmented in the operation 1204, or to avoid objectsidentified and/or tracked by a planner system of the autonomous vehicle.Further, the commands generated in the operation 1208 can be relayed toa controller onboard an autonomous vehicle to control the autonomousvehicle to drive the trajectory. Although discussed in the context of anautonomous vehicle, the process 1200, and the techniques and systemsdescribed herein, can be applied to a variety systems utilizing machinevision.

FIG. 13 depicts an example process 1300 for receiving segmentationinformation, adapting a rendering plane for the object, converting thethree-dimensional data of the object to two-dimensional data of theobject, and performing classification. For example, some or all of theprocess 1300 can be performed by one or more components in thearchitecture 300, or in the environment 1400, as described below.

At operation 1302, the process can include receiving segmentationinformation associated with a two-dimensional representation of data. Insome instances, the segmentation information can be generated by theprocesses described in connection with FIGS. 1 and/or 5.

At operation 1304, the process can include converting the segmentationinformation to three-dimensional segmentation information. For example,the two-dimensional representation of data may be related tothree-dimensional data, such as LIDAR data. Thus, the operation 1304 mayinclude associating the two-dimensional segmentation information withthe three-dimensional representation of the data, and associating depthinformation with the segmentation information. In some examples, suchsegmentation information may include a three dimensional bounding box.In such examples, the three dimensional bounding box may be used toselect a portion of the LIDAR data. In some instances, the segmentationinformation may include segmentation identifications (IDs) associatedwith each data point, such that a particular segmentation ID isassociated with a particular object. Thus, by selecting a particularsegmentation ID, data points associated with the segmentation ID can beidentified and extracted as a segmented object.

At operation 1306, the process can include applying thethree-dimensional segmentation information to a three-dimensionaldataset to identify a three-dimensional object. In some instances, theprocess 1300 may include performing classification on a per-objectbasis. Thus, the operation 1306 may include identifying, isolating,extracting, and/or segmenting the three-dimensional object from thethree-dimensional dataset so that any subsequent processing can beoptimized for the particular object. Further, by isolating and/orextracting the three-dimensional object, a performance of a computer maybe improved by reducing a dataset or an amount of data required forprocessing. That is, converting of data and/or classification may beperformed on a reduced amount of data.

At operation 1308, the process can include adapting or positioning arendering plane relative to the three-dimensional object to optimize,for example, a horizontal extent of data associated with thethree-dimensional object. In some instances, the operation 1308 mayinclude optimizing a vertical extent of the data in addition to orinstead of optimizing the horizontal extent of data. In some instances,this operation 1308 may include orienting the rendering plane to bestfit the data associated with the three-dimensional object. In someinstances, this operation may include locating a virtual LIDAR sensor ata position and orientation based on a principal component analysis ofthe segmented data.

At operation 1310, the process can include projecting the dataassociated with the three-dimensional object onto the rendering plane togenerate a two dimensional representation of the object. In someinstances, the operation 1310 may include determining a projection typefor projecting the data onto the rendering plane. For example, aprojection type may utilize perspective geometry with the virtual LIDARsensor as the focus of the perspective. By way of another example, aprojection type may utilize orthogonal geometry for projecting data tothe projection plane. In some instances, the rendering plane may includeany number of cells, corresponding to a resolution of the renderingplane.

At operation 1312, the process can include performing classification onthe two-dimensional representation of the object. For example, asdescribed herein, the operation 1312 may include inputting thetwo-dimensional representation of the object into a convolutional neuralnetwork trained on such projected data.

FIG. 14 illustrates an environment 1400 in which the disclosures may beimplemented in whole or in part. The environment 1400 depicts one ormore computer systems 1402 that comprise a storage 1404, one or moreprocessor(s) 1406, a memory 1408, and an operating system 1410. Thestorage 1404, the processor(s) 1406, the memory 1408, and the operatingsystem 1410 may be communicatively coupled over a communicationinfrastructure 1412. Optionally, the computer system 1402 may interactwith a user, or environment, via input/output (I/O) device(s) 1414, aswell as one or more other computing devices over a network 1416, via thecommunication infrastructure 1412. The operating system 1410 mayinteract with other components to control one or more applications 1418.

In some instances, the computer system(s) 1402 may correspond to thecomputer system(s) 302 of FIG. 3. Further, the computer system(s) 302may implement any hardware and/or software to implement the modules 304,306, 308, 310, 314, 316, 318, 320, and 322 to perform the converting ofmulti-dimensional data for image analysis, as discussed herein.

The systems and methods described herein can be implemented in softwareor hardware or any combination thereof. The systems and methodsdescribed herein can be implemented using one or more computing deviceswhich may or may not be physically or logically separate from eachother. The methods may be performed by components arranged as eitheron-premise hardware, on-premise virtual systems, or hosted-privateinstances. Additionally, various aspects of the methods described hereinmay be combined or merged into other functions.

An exemplary environment and computerized system for implementing thesystems and methods described herein is illustrated in FIG. 14. Aprocessor or computer system can be configured to particularly performsome or all of the methods described herein. In some embodiments, themethods can be partially or fully automated by one or more computers orprocessors. The systems and methods described herein may be implementedusing a combination of any of hardware, firmware and/or software. Thepresent systems and methods described herein (or any part(s) orfunction(s) thereof) may be implemented using hardware, software,firmware, or a combination thereof and may be implemented in one or morecomputer systems or other processing systems. In some embodiments, theillustrated system elements could be combined into a single hardwaredevice or separated into multiple hardware devices. If multiple hardwaredevices are used, the hardware devices could be physically locatedproximate to or remotely from each other. The embodiments of the methodsdescribed and illustrated are intended to be illustrative and not to belimiting. For example, some or all of the steps of the methods can becombined, rearranged, and/or omitted in different embodiments.

In one exemplary embodiment, the systems and methods described hereinmay be directed toward one or more computer systems capable of carryingout the functionality described herein. Example computing devices maybe, but are not limited to, a personal computer (PC) system running anyoperating system such as, but not limited to, OS X™ iOS™, Linux™,Android™, and Microsoft™ Windows™. However, the systems and methodsdescribed herein may not be limited to these platforms. Instead, thesystems and methods described herein may be implemented on anyappropriate computer system running any appropriate operating system.Other components of the systems and methods described herein, such as,but not limited to, a computing device, a communications device, mobilephone, a smartphone, a telephony device, a telephone, a personal digitalassistant (PDA), a personal computer (PC), a handheld PC, an interactivetelevision (iTV), a digital video recorder (DVD), client workstations,thin clients, thick clients, proxy servers, network communicationservers, remote access devices, client computers, server computers,routers, web servers, data, media, audio, video, telephony or streamingtechnology servers, etc., may also be implemented using a computingdevice. Services may be provided on demand using, e.g., but not limitedto, an interactive television (iTV), a video on demand system (VOD), andvia a digital video recorder (DVR), or other on demand viewing system.

The system may include one or more processors. The processor(s) may beconnected to a communication infrastructure, such as but not limited to,a communications bus, cross-over bar, or network, etc. The processes andprocessors need not be located at the same physical locations. In otherwords, processes can be executed at one or more geographically distantprocessors, over for example, a LAN or WAN connection. Computing devicesmay include a display interface that may forward graphics, text, andother data from the communication infrastructure for display on adisplay unit.

The computer system may also include, but is not limited to, a mainmemory, random access memory (RAM), and a secondary memory, etc. Thesecondary memory may include, for example, a hard disk drive and/or aremovable storage drive, such as a compact disc drive CD-ROM, etc. Theremovable storage drive may read from and/or written to a removablestorage unit. As may be appreciated, the removable storage unit mayinclude a computer usable storage medium having stored therein computersoftware and/or data. In some embodiments, a machine-accessible mediummay refer to any storage device used for storing data accessible by acomputer. Examples of a machine-accessible medium may include, e.g., butnot limited to: a magnetic hard disk; a floppy disk; an optical disk,like a compact disc read-only memory (CD-ROM) or a digital versatiledisc (DVD); a magnetic tape; and/or a memory chip, etc.

The processor may also include, or be operatively coupled to communicatewith, one or more data storage devices for storing data. Such datastorage devices can include, as non-limiting examples, magnetic disks(including internal hard disks and removable disks), magneto-opticaldisks, optical disks, read-only memory, random access memory, and/orflash storage. Storage devices suitable for tangibly embodying computerprogram instructions and data can also include all forms of non-volatilememory, including, for example, semiconductor memory devices, such asEPROM, EEPROM, and flash memory devices; magnetic disks such as internalhard disks and removable disks; magneto-optical disks; and CD-ROM andDVD-ROM discs. The processor and the memory can be supplemented by, orincorporated in, ASICs (application-specific integrated circuits).

The processing system can be in communication with a computerized datastorage system. The data storage system can include a non-relational orrelational data store, such as a MySQL™ or other relational database.Other physical and logical database types could be used. The data storemay be a database server, such as Microsoft SQL Server™, Oracle™, IBMDB2™, SQLITE™, or any other database software, relational or otherwise.The data store may store the information identifying syntactical tagsand any information required to operate on syntactical tags. In someembodiments, the processing system may use object-oriented programmingand may store data in objects. In these embodiments, the processingsystem may use an object-relational mapper (ORM) to store the dataobjects in a relational database. The systems and methods describedherein can be implemented using any number of physical data models. Inone example embodiment, a relational database management system (RDBMS)can be used. In those embodiments, tables in the RDBMS can includecolumns that represent coordinates. In the case of economic systems,data representing companies, products, etc. can be stored in tables inthe RDBMS. The tables can have pre-defined relationships between them.The tables can also have adjuncts associated with the coordinates.

In alternative exemplary embodiments, secondary memory may include othersimilar devices for allowing computer programs or other instructions tobe loaded into computer system. Such devices may include, for example, aremovable storage unit and an interface. Examples of such may include aprogram cartridge and cartridge interface (such as, e.g., but notlimited to, those found in video game devices), a removable memory chip(such as, e.g., but not limited to, an erasable programmable read onlymemory (EPROM), or programmable read only memory (PROM) and associatedsocket), and other removable storage units and interfaces, which mayallow software and data to be transferred from the removable storageunit to computer system.

The computing device may also include an input device such as, but notlimited to, a voice input device, such as a microphone, touch screens,gesture recognition devices, such as cameras, other natural userinterfaces, a mouse or other pointing device such as a digitizer, and akeyboard or other data entry device. The computing device may alsoinclude output devices, such as but not limited to, a display, and adisplay interface. The computing device may include input/output (I/O)devices such as but not limited to a communications interface, cable andcommunications path, etc. These devices may include, but are not limitedto, a network interface card, and modems. Communications interface(s)may allow software and data to be transferred between a computer systemand one or more external devices.

In one or more embodiments, the computing device may be operativelycoupled to an automotive system. Such automotive system may be eithermanually operated, semi-autonomous, or fully autonomous. In such anembodiment, input and output devices may include one or more imagecapture devices, controllers, microcontrollers, and/or other processorsto control automotive functions such as, but not limited to,acceleration, braking, and steering. Further, communicationinfrastructure in such embodiments may also include a Controller AreaNetwork (CAN) bus.

In one or more embodiments, the computing device may be operativelycoupled to any machine vision based system. For example, such machinebased vision systems include but are not limited to manually operated,semi-autonomous, or fully autonomous industrial or agricultural robots,household robot, inspection system, security system, etc. That is, theembodiments described herein are not limited to one particular contextand may be applicable to any application utilizing machine vision.

In one or more embodiments, the present embodiments can be practiced inthe environment of a computer network or networks. The network caninclude a private network, or a public network (for example theInternet, as described below), or a combination of both. The network mayinclude hardware, software, or a combination of both.

From a telecommunications-oriented view, the network can be described asa set of hardware nodes interconnected by a communications facility,with one or more processes (hardware, software, or a combinationthereof) functioning at each such node. The processes caninter-communicate and exchange information with one another viacommunication pathways between them using interprocess communicationpathways. On these pathways, appropriate communications protocols areused.

An exemplary computer and/or telecommunications network environment inaccordance with the present embodiments may include nodes, which mayinclude hardware, software, or a combination of hardware and software.The nodes may be interconnected via a communications network. Each nodemay include one or more processes, executable by processors incorporatedinto the nodes. A single process may be run by multiple processors, ormultiple processes may be run by a single processor, for example.Additionally, each of the nodes may provide an interface point betweennetwork and the outside world, and may incorporate a collection ofsub-networks.

In an exemplary embodiment, the processes may communicate with oneanother through interprocess communication pathways supportingcommunication through any communications protocol. The pathways mayfunction in sequence or in parallel, continuously or intermittently. Thepathways can use any of the communications standards, protocols ortechnologies, described herein with respect to a communications network,in addition to standard parallel instruction sets used by manycomputers.

The nodes may include any entities capable of performing processingfunctions. Examples of such nodes that can be used with the embodimentsinclude computers (such as personal computers, workstations, servers, ormainframes), handheld wireless devices and wireline devices (such aspersonal digital assistants (PDAs), modem cell phones with processingcapability, wireless email devices including BlackBerry™ devices),document processing devices (such as scanners, printers, facsimilemachines, or multifunction document machines), or complex entities (suchas local-area networks or wide area networks) to which are connected acollection of processors, as described. For example, in the context ofthe present disclosure, a node itself can be a wide-area network (WAN),a local-area network (LAN), a private network (such as a Virtual PrivateNetwork (VPN)), or collection of networks.

Communications between the nodes may be made possible by acommunications network. A node may be connected either continuously orintermittently with communications network. As an example, in thecontext of the present disclosure, a communications network can be adigital communications infrastructure providing adequate bandwidth andinformation security.

The communications network can include wireline communicationscapability, wireless communications capability, or a combination ofboth, at any frequencies, using any type of standard, protocol ortechnology. In addition, in the present embodiments, the communicationsnetwork can be a private network (for example, a VPN) or a publicnetwork (for example, the Internet).

A non-inclusive list of exemplary wireless protocols and technologiesused by a communications network may include Bluetooth™, general packetradio service (GPRS), cellular digital packet data (CDPD), mobilesolutions platform (MSP), multimedia messaging (MMS), wirelessapplication protocol (WAP), code division multiple access (CDMA), shortmessage service (SMS), wireless markup language (WML), handheld devicemarkup language (HDML), binary runtime environment for wireless (BREW),radio access network (RAN), and packet switched core networks (PS-CN).Also included are various generation wireless technologies. An exemplarynon-inclusive list of primarily wireline protocols and technologies usedby a communications network includes asynchronous transfer mode (ATM),enhanced interior gateway routing protocol (EIGRP), frame relay (FR),high-level data link control (HDLC), Internet control message protocol(ICMP), interior gateway routing protocol (IGRP), internetwork packetexchange (IPX), ISDN, point-to-point protocol (PPP), transmissioncontrol protocol/internet protocol (TCP/IP), routing informationprotocol (RIP) and user datagram protocol (UDP). As skilled persons willrecognize, any other known or anticipated wireless or wireline protocolsand technologies can be used.

Embodiments of the present disclosure may include apparatuses forperforming the operations herein. An apparatus may be speciallyconstructed for the desired purposes, or it may comprise a generalpurpose device selectively activated or reconfigured by a program storedin the device.

In one or more embodiments, the present embodiments are embodied inmachine-executable instructions. The instructions can be used to cause aprocessing device, for example a general-purpose or special-purposeprocessor, which is programmed with the instructions, to perform thesteps of the present disclosure. Alternatively, the steps of the presentdisclosure can be performed by specific hardware components that containhardwired logic for performing the steps, or by any combination ofprogrammed computer components and custom hardware components. Forexample, the present disclosure can be provided as a computer programproduct, as outlined above. In this environment, the embodiments caninclude a machine-readable medium having instructions stored on it. Theinstructions can be used to program any processor or processors (orother electronic devices) to perform a process or method according tothe present exemplary embodiments. In addition, the present disclosurecan also be downloaded and stored on a computer program product. Here,the program can be transferred from a remote computer (e.g., a server)to a requesting computer (e.g., a client) by way of data signalsembodied in a carrier wave or other propagation medium via acommunication link (e.g., a modem or network connection) and ultimatelysuch signals may be stored on the computer systems for subsequentexecution.

The methods can be implemented in a computer program product accessiblefrom a computer-usable or computer-readable storage medium that providesprogram code for use by or in connection with a computer or anyinstruction execution system. A computer-usable or computer-readablestorage medium can be any apparatus that can contain or store theprogram for use by or in connection with the computer or instructionexecution system, apparatus, or device.

A data processing system suitable for storing and/or executing thecorresponding program code can include at least one processor coupleddirectly or indirectly to computerized data storage devices such asmemory elements. Input/output (I/O) devices (including but not limitedto keyboards, displays, pointing devices, etc.) can be coupled to thesystem. Network adapters may also be coupled to the system to enable thedata processing system to become coupled to other data processingsystems or remote printers or storage devices through interveningprivate or public networks. To provide for interaction with a user, thefeatures can be implemented on a computer with a display device, such asan LCD (liquid crystal display), or another type of monitor fordisplaying information to the user, and a keyboard and an input device,such as a mouse or trackball by which the user can provide input to thecomputer.

A computer program can be a set of instructions that can be used,directly or indirectly, in a computer. The systems and methods describedherein can be implemented using programming languages such as CUDA,OpenCL, Flash™, JAVA™, C++, C, C #, Python, Visual Basic™, JavaScript™PHP, XML, HTML, etc., or a combination of programming languages,including compiled or interpreted languages, and can be deployed in anyform, including as a stand-alone program or as a module, component,subroutine, or other unit suitable for use in a computing environment.The software can include, but is not limited to, firmware, residentsoftware, microcode, etc. Protocols such as SOAP/HTTP may be used inimplementing interfaces between programming modules. The components andfunctionality described herein may be implemented on any desktopoperating system executing in a virtualized or non-virtualizedenvironment, using any programming language suitable for softwaredevelopment, including, but not limited to, different versions ofMicrosoft Windows™, Apple™ Mac™, iOS™, Unix™/X-Windows™, Linux™, etc.The system could be implemented using a web application framework, suchas Ruby on Rails.

Suitable processors for the execution of a program of instructionsinclude, but are not limited to, general and special purposemicroprocessors, and the sole processor or one of multiple processors orcores, of any kind of computer. A processor may receive and storeinstructions and data from a computerized data storage device such as aread-only memory, a random access memory, both, or any combination ofthe data storage devices described herein. A processor may include anyprocessing circuitry or control circuitry operative to control theoperations and performance of an electronic device.

The systems, modules, and methods described herein can be implementedusing any combination of software or hardware elements. The systems,modules, and methods described herein can be implemented using one ormore virtual machines operating alone or in combination with one other.Any applicable virtualization solution can be used for encapsulating aphysical computing machine platform into a virtual machine that isexecuted under the control of virtualization software running on ahardware computing platform or host. The virtual machine can have bothvirtual system hardware and guest operating system software.

The systems and methods described herein can be implemented in acomputer system that includes a back-end component, such as a dataserver, or that includes a middleware component, such as an applicationserver or an Internet server, or that includes a front-end component,such as a client computer having a graphical user interface or anInternet browser, or any combination of them. The components of thesystem can be connected by any form or medium of digital datacommunication such as a communication network. Examples of communicationnetworks include, e.g., a LAN, a WAN, and the computers and networksthat form the Internet.

One or more embodiments of the present disclosure may be practiced withother computer system configurations, including hand-held devices,microprocessor systems, microprocessor-based or programmable consumerelectronics, minicomputers, mainframe computers, etc. The systems andmethods described herein may also be practiced in distributed computingenvironments where tasks are performed by remote processing devices thatare linked through a network.

The terms “computer program medium” and “computer readable medium” maybe used to generally refer to media such as but not limited to removablestorage drive, a hard disk installed in hard disk drive. These computerprogram products may provide software to computer system. The systemsand methods described herein may be directed to such computer programproducts.

References to “one embodiment,” “an embodiment,” “example embodiment,”“various embodiments,” etc., may indicate that the embodiment(s) of thepresent disclosure may include a particular feature, structure, orcharacteristic, but not every embodiment necessarily includes theparticular feature, structure, or characteristic. Further, repeated useof the phrase “in one embodiment,” or “in an exemplary embodiment,” donot necessarily refer to the same embodiment, although they may.Similarly, references to “instances” may indicate that variousinstance(s) of the present disclosure may include a particular feature,structure, or characteristic, but not every instance necessarilyincludes the particular feature, structure, or characteristic. Further,repeated use of the phrase “in some instances” does not necessarilyrefer to the same instance, although it may.

In the description and claims, the terms “coupled” and “connected,”along with their derivatives, may be used. It should be understood thatthese terms may be not intended as synonyms for each other. Rather, inparticular embodiments, “connected” may be used to indicate that two ormore elements are in direct physical or electrical contact with eachother. “Coupled” may mean that two or more elements are in directphysical or electrical contact. However, “coupled” may also mean thattwo or more elements are not in direct contact with each other, but yetstill co-operate or interact with each other.

An algorithm may be here, and generally, considered to be aself-consistent sequence of acts or operations leading to a desiredresult. These include physical manipulations of physical quantities.Usually, though not necessarily, these quantities take the form ofelectrical or magnetic signals capable of being stored, transferred,combined, compared, and otherwise manipulated. It has proven convenientat times, principally for reasons of common usage, to refer to thesesignals as bits, values, elements, symbols, characters, terms, numbersor the like. It should be understood, however, that all of these andsimilar terms are to be associated with the appropriate physicalquantities and are merely convenient labels applied to these quantities.

Unless specifically stated otherwise, it may be appreciated thatthroughout the specification terms such as “processing,” “computing,”“calculating,” “determining,” or the like, refer to the action and/orprocesses of a computer or computing system, or similar electroniccomputing device, that manipulate and/or transform data represented asphysical, such as electronic, quantities within the computing system'sregisters and/or memories into other data similarly represented asphysical quantities within the computing system's memories, registers orother such information storage, transmission or display devices.

In a similar manner, the term “processor” may refer to any device orportion of a device that processes electronic data from registers and/ormemory to transform that electronic data into other electronic data thatmay be stored in registers and/or memory. As non-limiting examples,“processor” may be a Central Processing Unit (CPU) or a GraphicsProcessing Unit (GPU). A “computing platform” may comprise one or moreprocessors. As used herein, “software” processes may include, forexample, software and/or hardware entities that perform work over time,such as tasks, threads, and intelligent agents. Also, each process mayrefer to multiple processes, for carrying out instructions in sequenceor in parallel, continuously or intermittently. The terms “system” and“method” are used herein interchangeably insofar as the system mayembody one or more methods and the methods may be considered as asystem.

While one or more embodiments have been described, various alterations,additions, permutations and equivalents thereof are included within thescope of the disclosure.

In the description of embodiments, reference is made to the accompanyingdrawings that form a part hereof, which show by way of illustrationspecific embodiments of the claimed subject matter. It is to beunderstood that other embodiments may be used and that changes oralterations, such as structural changes, may be made. Such embodiments,changes or alterations are not necessarily departures from the scopewith respect to the intended claimed subject matter. While the stepsherein may be presented in a certain order, in some cases the orderingmay be changed so that certain inputs are provided at different times orin a different order without changing the function of the systems andmethods described. The disclosed procedures could also be executed indifferent orders. Additionally, various computations that are hereinneed not be performed in the order disclosed, and other embodimentsusing alternative orderings of the computations could be readilyimplemented. In addition to being reordered, the computations could alsobe decomposed into sub-computations with the same results.

Although the discussion above sets forth example implementations of thedescribed techniques, other architectures may be used to implement thedescribed functionality, and are intended to be within the scope of thisdisclosure. Furthermore, although specific distributions ofresponsibilities are defined above for purposes of discussion, thevarious functions and responsibilities might be distributed and dividedin different ways, depending on circumstances.

Furthermore, although the subject matter has been described in languagespecific to structural features and/or methodological acts, it is to beunderstood that the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described. Rather,the specific features and acts are disclosed as exemplary forms ofimplementing the claims.

EXAMPLE CLAUSES

A. An example system comprises:

one or more processors; and

one or more computer readable storage media communicatively coupled tothe one or more processors and storing instructions that are executableby the one or more processors to:

-   -   receive a three-dimensional dataset captured by at least one        LIDAR sensor installed on an autonomous vehicle, the at least        one LIDAR sensor associated with a first perspective;    -   receive segmentation information associated with the        three-dimensional dataset, the segmentation information        including, for an individual data point of the three-dimensional        dataset, a segmentation identifier associated with a particular        object;    -   select a portion of the three-dimensional dataset based at least        in part on the segmentation identifier;    -   extract, as extracted data, the portion of the three-dimensional        dataset, wherein the extracted data represents a first width and        a first height relative to the first perspective;    -   perform a principal component analysis on the extracted data to        determine at least a first principal component, a second        principal component, and a third principal component of the        extracted data;    -   position a rendering plane relative to the extracted data based        at least in part on the principal component analysis, wherein an        orientation of the rendering plane is associated with a second        perspective different than the first perspective, wherein the        orientation of the rendering plane is selected such that the        extracted data represents a second width and a second height        relative to the second perspective, and wherein the second width        is greater than the first width;    -   project, as projected data, the extracted data onto the        rendering plane, the projected data representing a        two-dimensional representation of the extracted data; and    -   perform classification on the projected data to determine an        object classification associated with the projected data.

B. The system of example A, wherein a classification function performingthe classification includes at least one convolutional neural network todetermine the object classification associated with the projected data.

C. The system of example A or example B, wherein the orientation of therendering plane is selected to substantially maximize the second widthassociated with the extracted data relative to the rendering plane.

D. The system of any one of example A through example C, wherein thesecond height is greater than the first height, and wherein theorientation of the rendering plane is selected to substantially maximizethe second height associated with the extracted data relative to therendering plane.

E. The system of any one of example A through example C, wherein thesegmentation information is associated with a plurality of objectsrepresented in the three-dimensional dataset, and wherein a firstrendering plane is positioned relative to a first object of theplurality of objects and a second rendering plane is positioned relativeto a second object of the plurality of objects.

F. The system of any one of example A through example C, wherein the atleast one LIDAR sensor includes a plurality of LIDAR sensors, andwherein the three-dimensional dataset includes LIDAR data fused fromdata captured by the plurality of LIDAR sensors.

G. The system of any one of example A through example C, wherein a sizeof the rendering plane is based at least in part on an input image sizeassociated with a classification function performing the classification.

H. An example method comprises:

receiving segmentation information associated with a dataset, thedataset including three-dimensional data captured with respect to afirst perspective;

extracting, as extracted data, a portion of the dataset, wherein theextracting is based at least in part on the segmentation information;

positioning a rendering plane relative to the extracted data, wherein anorientation of the rendering plane is associated with a secondperspective;

projecting, as projected data, the extracted data onto the renderingplane; and

performing classification on the projected data to determine an objectclassification of an object represented in the projected data.

I. The method of example H, wherein the segmentation informationincludes at least a three-dimensional bounding box, the method furthercomprising extracting the portion of the three-dimensional dataset boundwithin the three-dimensional bounding box.

J. The method of example H or example I, wherein the segmentationinformation includes at least a segmentation identifier associated withindividual data points of the dataset, the method further comprising:

selecting the segmentation identifier from a plurality of segmentationidentifiers; and

extracting, as the extracted data, the portion of the dataset based atleast in part on the segmentation identifier.

K. The method of any one of example H through example J, wherein:

the extracted data represents a first width and a first height relativeto the first perspective; and

wherein the orientation of the rendering plane is selected such that theextracted data represents a second width and a second height relative tothe second perspective, and wherein the second width is greater than thefirst width.

L. The method of any one of example H through example J, wherein theperforming the classification on the projected data includes providingthe projected data to a convolutional neural network trained to classifyobjects represented in two-dimensional data.

M. The method of any one of example H through example J, furthercomprising:

determining a first variance of the dataset projected onto a planeassociated with the first perspective, the first variance associatedwith an orientation;

determining a second variance of the dataset projected onto therendering plane, the second variance associated with the orientation;and

selecting the orientation of the rendering plane based at least in parton the second variance being higher than the first variance.

N. The method of any one of example H through example J, wherein:

the positioning the rendering plane relative to the extracted dataincludes at least performing a principal component analysis on theextracted data; and

the orientation of the rendering plane is based at least in part on theprincipal component analysis.

O. The method of any one of example H through example J, wherein theprojecting the extracted data onto the rendering plane converts theportion of the three-dimensional dataset into two-dimensional data.

P. The method of any one of example H through example J, wherein thedataset represents at least one of:

LIDAR data captured by one or more LIDAR sensors; or

simulated LIDAR data generated by a simulator.

Q. An exemplary system comprises:

one or more processors; and

one or more computer readable storage media communicatively coupled tothe one or more processors and storing instructions that are executableby the one or more processors to:

-   -   receive segmentation information associated with a dataset, the        dataset representing three-dimensional data captured with        respect to a first perspective;    -   extract, as extracted data, a portion of the dataset, wherein        the extracted data is based at least in part on the segmentation        information;    -   position a rendering plane relative to the extracted data,        wherein an orientation of the rendering plane is associated with        a second perspective;    -   project, as projected data, the extracted data onto the        rendering plane; and    -   perform classification on the projected data to determine an        object classification of an object represented in the projected        data.

R. A system of example Q, wherein the second perspective is differentthan the first perspective.

S. A system of example R, wherein the instructions are furtherexecutable by the one or more processors to track the object representedin the projected data based at least in part upon which to generate atrajectory for the autonomous vehicle.

T. A system of example R or example S, wherein:

the extracted data represents a first width and a first height relativeto the first perspective; and

wherein the orientation of the rendering plane is selected such that theextracted data represents a second width and a second height relative tothe second perspective, and wherein the second width is greater than thefirst width.

What is claimed is:
 1. A system comprising: one or more processors; andone or more non-transitory computer readable storage mediacommunicatively coupled to the one or more processors and storinginstructions that are executable by the one or more processors to:receive a three-dimensional dataset captured by at least one LIDARsensor installed on an autonomous vehicle, the at least one LIDAR sensorassociated with a first perspective; receive segmentation informationassociated with the three-dimensional dataset, the segmentationinformation including, for an individual data point of thethree-dimensional dataset, a segmentation identifier associated with aparticular object; select a portion of the three-dimensional datasetbased at least in part on the segmentation identifier; extract, asextracted data, the portion of the three-dimensional dataset, whereinthe extracted data represents a first width and a first height relativeto the first perspective; perform a principal component analysis on theextracted data to determine at least a first principal component, asecond principal component, and a third principal component of theextracted data; position a rendering plane relative to the extracteddata based at least in part on the principal component analysis, whereinan orientation of the rendering plane is associated with a secondperspective different than the first perspective, wherein theorientation of the rendering plane is selected such that the extracteddata represents a second width and a second height relative to thesecond perspective, and wherein the second width is greater than thefirst width; project, as projected data, the extracted data onto therendering plane, the projected data representing a two-dimensionalrepresentation of the extracted data; and perform classification on theprojected data to determine an object classification associated with theprojected data.
 2. The system of claim 1, wherein a classificationfunction performing the classification includes at least oneconvolutional neural network to determine the object classificationassociated with the projected data.
 3. The system of claim 1, whereinthe orientation of the rendering plane is selected to substantiallymaximize the second width associated with the extracted data relative tothe rendering plane.
 4. The system of claim 1, wherein the second heightis greater than the first height, and wherein the orientation of therendering plane is selected to substantially maximize the second heightassociated with the extracted data relative to the rendering plane. 5.The system of claim 1, wherein the segmentation information isassociated with a plurality of objects represented in thethree-dimensional dataset, and wherein a first rendering plane ispositioned relative to a first object of the plurality of objects and asecond rendering plane is positioned relative to a second object of theplurality of objects.
 6. The system of claim 1, wherein the at least oneLIDAR sensor includes a plurality of LIDAR sensors, and wherein thethree-dimensional dataset includes LIDAR data fused from data capturedby the plurality of LIDAR sensors.
 7. The system of claim 1, wherein asize of the rendering plane is based at least in part on an input imagesize associated with a classification function performing theclassification.
 8. A method comprising: receiving segmentationinformation associated with a dataset, the dataset includingthree-dimensional data captured with respect to a first perspective;extracting, as extracted data, a portion of the dataset, wherein theextracting is based at least in part on the segmentation information;positioning a rendering plane relative to the extracted data, wherein anorientation of the rendering plane is associated with a secondperspective; projecting, as projected data, the extracted data onto therendering plane; and performing classification on the projected data todetermine an object classification of an object represented in theprojected data.
 9. The method of claim 8, wherein the segmentationinformation includes at least a three-dimensional bounding box, themethod further comprising extracting the portion of the dataset boundwithin the three-dimensional bounding box.
 10. The method of claim 8,wherein the segmentation information includes at least a segmentationidentifier associated with individual data points of the dataset, themethod further comprising: selecting the segmentation identifier from aplurality of segmentation identifiers; and extracting, as the extracteddata, the portion of the dataset based at least in part on thesegmentation identifier.
 11. The method of claim 8, wherein: theextracted data represents a first width and a first height relative tothe first perspective; and wherein the orientation of the renderingplane is selected such that the extracted data represents a second widthand a second height relative to the second perspective, and wherein thesecond width is greater than the first width.
 12. The method of claim 8,wherein the performing the classification on the projected data includesproviding the projected data to a convolutional neural network trainedto classify objects represented in two-dimensional data.
 13. The methodof claim 8, wherein the orientation is a first orientation, the methodfurther comprising: determining a first variance of the datasetprojected onto a plane associated with the first perspective, the firstvariance associated with a second orientation; determining a secondvariance of the dataset projected onto the rendering plane, the secondvariance associated with the first orientation; and selecting the firstorientation of the rendering plane based at least in part on the secondvariance being higher than the first variance.
 14. The method of claim8, wherein: the positioning the rendering plane relative to theextracted data includes at least performing a principal componentanalysis on the extracted data; and the orientation of the renderingplane is based at least in part on the principal component analysis. 15.The method of claim 8, wherein the projecting the extracted data ontothe rendering plane converts the portion of the dataset intotwo-dimensional data.
 16. The method of claim 8, wherein the datasetrepresents at least one of: LIDAR data captured by one or more LIDARsensors; or simulated LIDAR data generated by a simulator.
 17. A systemcomprising: one or more processors; and one or more non-transitorycomputer readable storage media communicatively coupled to the one ormore processors and storing instructions that are executable by the oneor more processors to: receive segmentation information associated witha dataset, the dataset representing three-dimensional data captured withrespect to a first perspective; extract, as extracted data, a portion ofthe dataset, wherein the extracted data is based at least in part on thesegmentation information; position a rendering plane relative to theextracted data, wherein an orientation of the rendering plane isassociated with a second perspective; project, as projected data, theextracted data onto the rendering plane; and perform classification onthe projected data to determine an object classification of an objectrepresented in the projected data.
 18. The system of claim 17, whereinthe second perspective is different than the first perspective.
 19. Thesystem of claim 17, wherein the instructions are further executable bythe one or more processors to generate a trajectory for an autonomousvehicle based at least in part on the object represented in theprojected data.
 20. The system of claim 17, wherein: the extracted datarepresents a first width and a first height relative to the firstperspective; and wherein the orientation of the rendering plane isselected such that the extracted data represents a second width and asecond height relative to the second perspective, and wherein the secondwidth is greater than the first width.